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Indexing handwriting using word matching

Authors:

R. Manmatha,

Chengfeng Han,

E. M. Riseman,

W. B. CroftAuthors Info & Claims

DL '96: Proceedings of the first ACM international conference on Digital libraries

Pages 151 - 159

https://doi.org/10.1145/226931.226960

Published: 01 April 1996 Publication History

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Cited By

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Gurav AJensen JKrishnan NChanda S(2023)ResPho(SC)Net: A Zero-Shot Learning Framework for Norwegian Handwritten Word Image RecognitionPattern Recognition and Image Analysis10.1007/978-3-031-36616-1_15(182-196)Online publication date: 25-Jun-2023
https://doi.org/10.1007/978-3-031-36616-1_15
Sapkal AChhavi Sharma SKumar PYadav S(2021)Keyword spotting in historical document collections withoutsegmentation using the Siamese Network2021 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES)10.1109/ICSES52305.2021.9633920(1-5)Online publication date: 24-Sep-2021
https://doi.org/10.1109/ICSES52305.2021.9633920
Kumari LSharma A(2021)A Review of Deep Learning Techniques in Document Image Word SpottingArchives of Computational Methods in Engineering10.1007/s11831-021-09605-7Online publication date: 17-May-2021
https://doi.org/10.1007/s11831-021-09605-7
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Indexing handwriting using word matching

Recommendations

Analysis of Handwriting Individuality Using Word Features
ICDAR '03: Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2

Analysis of allographs (characters) and allograph combinations(words) is the key for obtaining the discriminatingelements of handwriting. While allographs usually inhabitin words and segregation of a word into allographs ismore subjective than objective, ...
Word Spottin: A New Approach to Indexing Handwriting
On-Line Handwriting Word Recognition Using a Bi-character Model
ICPR '10: Proceedings of the 2010 20th International Conference on Pattern Recognition

This paper deals with on-line handwriting recognition. Analytic approaches have attracted an increasing interest during the last ten years. These approaches rely on a preliminary segmentation stage, which remains one of the most difficult problems and ...

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Published In

DL '96: Proceedings of the first ACM international conference on Digital libraries

April 1996

171 pages

ISBN:0897918304

DOI:10.1145/226931

Editors:
Edward A. Fox
Virginia Tech., Blacksburg
,
Gary Marchionini
Univ. of Maryland, College Park

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 April 1996

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DL96

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DL96: 1st ACM International Conference on Digital Libraries

March 20 - 23, 1996

Maryland, Bethesda, USA

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Overall Acceptance Rate 95 of 346 submissions, 27%

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Cited By

View all

Gurav AJensen JKrishnan NChanda S(2023)ResPho(SC)Net: A Zero-Shot Learning Framework for Norwegian Handwritten Word Image RecognitionPattern Recognition and Image Analysis10.1007/978-3-031-36616-1_15(182-196)Online publication date: 25-Jun-2023
https://doi.org/10.1007/978-3-031-36616-1_15
Sapkal AChhavi Sharma SKumar PYadav S(2021)Keyword spotting in historical document collections withoutsegmentation using the Siamese Network2021 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES)10.1109/ICSES52305.2021.9633920(1-5)Online publication date: 24-Sep-2021
https://doi.org/10.1109/ICSES52305.2021.9633920
Kumari LSharma A(2021)A Review of Deep Learning Techniques in Document Image Word SpottingArchives of Computational Methods in Engineering10.1007/s11831-021-09605-7Online publication date: 17-May-2021
https://doi.org/10.1007/s11831-021-09605-7
Riba PMolina AGomez LRamos-Terrades OLladós J(2021)Learning to Rank Words: Optimizing Ranking Metrics for Word SpottingDocument Analysis and Recognition – ICDAR 202110.1007/978-3-030-86331-9_25(381-395)Online publication date: 2-Sep-2021
https://doi.org/10.1007/978-3-030-86331-9_25
Shiah C(2020)Content-Based Document Image Retrieval Based on Document ModelingJournal of Intelligent Information Systems10.1007/s10844-020-00600-1Online publication date: 6-Jun-2020
https://doi.org/10.1007/s10844-020-00600-1
Serdouk YEglin VBres SPardoen M(2019)KeyWord Spotting using Siamese Triplet Deep Neural Networks2019 International Conference on Document Analysis and Recognition (ICDAR)10.1109/ICDAR.2019.00187(1157-1162)Online publication date: Sep-2019
https://doi.org/10.1109/ICDAR.2019.00187
Barakat BAlasam REl-Sana J(2018)Word Spotting Using Convolutional Siamese Network2018 13th IAPR International Workshop on Document Analysis Systems (DAS)10.1109/DAS.2018.67(229-234)Online publication date: Apr-2018
https://doi.org/10.1109/DAS.2018.67
Bogacz BMara H(2018)From Extraction to Spotting for Cuneiform Script Analysis2018 13th IAPR International Workshop on Document Analysis Systems (DAS)10.1109/DAS.2018.56(199-204)Online publication date: Apr-2018
https://doi.org/10.1109/DAS.2018.56
Aldavert DRusinol M(2018)Synthetically Generated Semantic Codebook for Bag-of-Visual-Words Based Word Spotting2018 13th IAPR International Workshop on Document Analysis Systems (DAS)10.1109/DAS.2018.25(223-228)Online publication date: Apr-2018
https://doi.org/10.1109/DAS.2018.25
Sando KSuzuki TAiba A(2018)A Constraint Solving Web Service for a Handwritten Japanese Historical Kana Reprint Support SystemAgents and Artificial Intelligence10.1007/978-3-030-05453-3_20(422-442)Online publication date: 30-Dec-2018
https://doi.org/10.1007/978-3-030-05453-3_20
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Analysis of Handwriting Individuality Using Word Features

Word Spottin: A New Approach to Indexing Handwriting

On-Line Handwriting Word Recognition Using a Bi-character Model